Venture Catalysts, India's first, largest and pioneering integrated incubator and accelerator platform, has invested an undisclosed amount in CUSMAT – a startup that builds high immersion training systems for enterprises moving metrics across productivity, safety and customer satisfaction. The seed funding round was led by Venture Catalysts investor – Raveen Sastry of Multiply Ventures. Co-investors Vaibhav Domkundwar, Better Capital, Rakesh Verma Chairman, MapMyIndia, Pratap Atwal, Director, CIPL (coronation Mining & Infra) also participated in the fund raise. Founded by three NIT Warangal, 2016 graduates Abhinav Ayan (CEO), Anirban Jyoti Chakravorty (CTO) and Soumyaranjan Harichandan (Head of Product), CUSMAT leverages AR/VR/MR and AI-based technologies to skill, upskill, train and assess people in enterprises. The company currently offers 5 training products, catering to more than 15 industries including Logistics, Electronics, Manufacturing, Mining, Steel, Cement, Pharmaceutical and Healthcare, among others.
Infosys has announced a strategic partnership with Lanxess for digitizing the IT infrastructure and enable its global workforce spread across 33 countries with a secure and fully managed modern workplace. As part of this transformation, Infosys will setup an end-user centric modern workplace with globally standardized device/workplace landscape (for Office, Functional and Virtual users) based on a Device as a Service (DaaS) construct, backed with NextGen unified communication and collaboration platforms. The global workforce of Lanxess will be supported by a multi-lingual artificial intelligence-powered service desk operating from Europe and India. Infosys will also transform Lanxess to a future-ready end user IT landscape over the course of the partnership. This will ensure a seamless and harmonized workplace experience for Lanxess' global workforce.
Researchers at the University of Liverpool have built an intelligent, mobile, robotic scientist that can solve a range of research problems. The robot seen here can work almost 24-7, carrying out experiments by itself. The automated scientist – the first of its kind – can make its own decisions about which chemistry experiments to perform next, and has already discovered a new catalyst. With humanoid dimensions, and working in a standard laboratory, it uses instruments much like a human does. Unlike a real person, however, this 400 kg robot has infinite patience, and works for 21.5 hours each day, pausing only to recharge its battery.
Land-use change by humans, particularly forest loss, is influencing Earth's biodiversity through time. To assess the influence of forest loss on population and biodiversity change, Daskalova et al. integrated data from more than 6000 time series of species' abundance, richness, and composition in ecological assemblages around the world. Forest loss leads to both positive and negative responses of populations and biodiversity, and the temporal lags in population and biodiversity change after forest loss can extend up to half a century. This analysis has consequences for projections of human impact, ongoing conservation, and assessments of biodiversity change.
Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene--a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class. If run using wind or solar power, the system also provides an efficient way to store electricity from these renewable but intermittent sources. "Using clean electricity to convert CO2 into ethylene, which has a $60 billion global market, can improve the economics of both carbon capture and clean energy storage," says Professor Ted Sargent, one of the senior authors on a new paper published today in Nature.
We present Catalyst.RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL) research. Main features of Catalyst.RL include large-scale asynchronous distributed training, efficient implementations of various RL algorithms and auxiliary tricks, such as n-step returns, value distributions, hyperbolic reinforcement learning, etc. To demonstrate the effectiveness of Catalyst.RL, we applied it to a physics-based reinforcement learning challenge "NeurIPS 2019: Learn to Move -- Walk Around" with the objective to build a locomotion controller for a human musculoskeletal model. The environment is computationally expensive, has a high-dimensional continuous action space and is stochastic. Our team took the 2nd place, capitalizing on the ability of Catalyst.RL to train high-quality and sample-efficient RL agents in only a few hours of training time. The implementation along with experiments is open-sourced so results can be reproduced and novel ideas tried out.
One-day research workshops on the application of AI approaches to a dedicated area of research (e.g. Workshops may be held in any Canadian city, but must include participants from multiple research institutions (universities, research institutes, research hospitals). The goal of the workshop should be to identify opportunities for the application of AI to the specific domain of interest, identify emerging research opportunities and foster the development of new collaborations. Up to $20,000 of funding is available and applicants will be asked to provide a complete budget. CIFAR will provide some logistical support to workshop organizers (e.g.
While artificial intelligence offers opportunities to automate and innovate, just 30% of workplaces are actually using it. Combined with a lack of understanding of the technology, employers don't have the internal structure and personnel needed to launch the power of AI into their business model, says Augustine Walker, senior director of product management for Veritone, an AI solutions provider. "There isn't a lot of focus on what tools are out there so that I can make my business better with AI," Walker says. "The ubiquity of the talent pool and the capabilities are not out there yet -- it's still maturing." Walker spoke with Employee Benefit News on how AI can actually be a catalyst for creativity and why data scientists are a critical piece to the puzzle.
We introduce a generic scheme for accelerating first-order optimization methods in the sense of Nesterov, which builds upon a new analysis of the accelerated proximal point algorithm. Our approach consists of minimizing a convex objective by approximately solving a sequence of well-chosen auxiliary problems, leading to faster convergence. This strategy applies to a large class of algorithms, including gradient descent, block coordinate descent, SAG, SAGA, SDCA, SVRG, Finito/MISO, and their proximal variants. For all of these methods, we provide acceleration and explicit support for non-strongly convex objectives. In addition to theoretical speed-up, we also show that acceleration is useful in practice, especially for ill-conditioned problems where we measure significant improvements.
The AIOps Catalyst team's work has resulted in a new collaborative workstream focused around the topic within TM Forum. Artificial intelligence (AI) offers huge opportunities for communications service providers (CSPs) to do things better, faster and cheaper. In fact, they have no choice but to introduce AI into operations and business processes due to growing complexity and the sheer volume of data and transactions. However, as well as delivering huge benefits, the introduction of AI also creates new challenges relating to the management of services and processes. A TM Forum Catalyst team is taking a two-pronged approach, tackling both these areas simultaneously to ensure CSPs – and their customers – reap the rewards of AI.